Pengembangan robot atau mesin untuk membantu kegiatan pertanian memerlukan riset yang panjang. Teknologi tersebut harus dapat memiliki keahlian dalam melakukan berbagai macam aktivitas dan mampu mendeteksi objek menjadi sasaran pekerjaannya. Untuk memenuhi hal ini, pertanian, misalnya buah, salah satu agenda perlu dilakukan dikembangkan. Tujuan penelitian ini adalah mengetahui hasil perbandinga...

Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive work, as it has to compute million of parameters resulting to huge consumption of memory. Moreover, extracting finer features and conducting supervised training tends to increase the complexity furthermore. With the introduction of Fully Convolutional Neural Network, which uses finer ...

Tumors in the brain are masses or clusters of abnormal cells that may spread to other tissues nearby and pose a danger patient. The main imaging technique used determine extent tumors is magnetic resonance imaging, which ensures an accurate diagnosis. A sizable amount data for model training advances designs provide better approximations supervised environment likely account most growth Deep Le...

Due to the increased disease occurrence rates in humans, need for Automated Disease Diagnosis (ADD) systems is also raised. Most of ADD are proposed support doctor during screening and decision making process. This research aims at developing a Computer Aided (CADD) scheme categorize brain tumour 2D MRI slices into Glioblastoma/Glioma class with better accuracy. The main contribution this work ...

Defects are unavoidable in casting production owing to the complexity of process. While conventional human-visual inspection products is slow and unproductive mass productions, an automatic reliable defect detection not just enhances quality control process but positively improves productivity. However, a challenging task due diversity variation defects' appearance. Convolutional neural network...

Artificial intelligence has many applications in various industries, including agriculture. It can help overcome challenges by providing efficient solutions, especially the early stages of development. When working with tree leaves to identify type disease, diseases often show up through changes leaf color. Therefore, it is crucial improve color brightness before using them intelligent agricult...

Deep learning models are widely used for solving problems in different applications. Especially Convolutional Neural Network (CNN) based found suitable medical image analysis. As brain stroke is increasing alarming rate, it essential to have better approaches detect time. Brain MRI one of the imaging technologies imaging.we proposed certain advancements well-known deep like VGG16, ResNet50 and ...

Diabetic foot ulcer (DFU) is one of the major complications diabetes and results in amputation lower limb if not treated timely properly. Despite traditional clinical approaches used DFU classification, automatic methods based on a deep learning framework show promising results. In this paper, we present several end-to-end CNN-based architectures, i.e., AlexNet, VGG16/19, GoogLeNet, ResNet50.10...

A deep learning model gives an incredible result for image processing by studying from the trained dataset. Spinach is a leaf vegetable that contains vitamins and nutrients. In our research, Deep method has been used can automatically identify spinach this dataset of total five species 3785 images. Four Convolutional Neural Network (CNN) models were to classify spinach. These give more accurate...

Deep Convolutional Neural Networks (CNN) have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene labeling, due to their large learning capacity and resistance to overfit. For the image classification task, most of the current deep CNNbased approaches take the whole size-normalized image as input and have achieved quite pro...